1. Field of the Invention
The present invention relates to a color discrimination data input apparatus utilized in a variety of fields requiring detection of color information of an object, as in administration of dyeing colors and painting colors, color measurements of products, color classification, and color measurements in the fields of medical and scientific applications.
2. Description of the Related Art
A conventional color measuring apparatus measures intensities of reflected light in wavelength regions including three primary colors, i.e., red (R), green (G), and blue (B) from reflected light spectra obtained by spectrally analyzing light reflected by a target object, and converts the measured data into numeric values in accordance with a chromaticity chart as of an XYZ display color system standardized by the Commission Internationale de l'Echairage (CIE) or the International Commission on Illumination, and represents colors by these numeric values.
In a conventional general color image input/output apparatus, image data of R, G, and B, or cyan, magenta, and yellow as complementary colors of R, G, and B are received, and these image data are processed. In color image transmission and recording, as defined in the NTSC (National Television System Committee) standards, R, G, and B signals are converted into a luminance signal (Y) and color difference data (e.g., I and Q), and a relatively wide bandwidth is assigned to the luminance signal, thereby obtaining high efficiency
The conventional techniques for processing color images as described above are based on color engineering having psychophysics as its background. In any conventional technique, image data of three primary colors, i.e., R, G, and B are utilized.
In order to detect a small difference between colors of objects, it is difficult to accurately discriminate differences between specific colors within a given image in color measurement based on R, G, and B measurement values. When colors have different spectra within a wavelength range of a G color matching function, these colors cannot be clearly discriminated from each other in accordance with G-B color classification.
A multichannel photometer is known as a photometer for measuring a spectrum of light reflected by an object and discriminating colors in accordance with differences in spectra.
This photometer requires expensive units such as a diffraction grating and a high-sensitivity detector array. In addition, when a reflected light spectrum is to be measured, the number of dimensional degrees per unit data is increased, so that an apparatus for processing and analyzing the spectrum data becomes bulky and complicated at high cost, resulting in inconvenience.
The present inventors made extensive studies on optimization of a wavelength range for receiving image data at the time of color information input in accordance with application purposes. The following statistic method is available as a means for determining a wavelength range.
A large number of objects whose correspondences between categories (classes) and spectra of light components reflected by the objects upon irradiation with predetermined illumination light and upon analysis of the reflected light are already known are prepared in units of classes. Spectra of light components reflected by objects prepared in units of classes upon radiation with illumination light are measured. In this manner, a data string of reflected light spectra obtained for each class is called a training set.
The spectrum data of the training set are statistically analyzed such that the intensity of reflected light at each wavelength i plotted along the ordinate and the wavelength is plotted along the abscissa, and that the wavelength range is equidistantly sampled n times to obtain n-dimensional vector data having reflected light intensities of the respective wavelengths as vector elements. The following mathematical technique may be applied as a technique for statistically classifying spectrum patterns prepared as training sets. More specifically, a Foley-Sammon-Trasform (F-S transform) described as a mathematical technique in IEEE Trans. Comp., C-24, 281, (1975) (Reference 1), D. H. Foley and J. W. Sammon Jr., is applied to the above spectrum patterns. That is, the reflected light spectra are classified into two classes in accordance with the F-S transform.
This is a method of obtaining (so as to optimize) vectors suitably classified into two classes in an n-dimensional space having the respective elements of the n-dimensional vector data as orthogonal axes in accordance with an evaluation reference called a Fisher ratio. By using a filter having wavelength characteristics corresponding to classified vectors derived from the F-S transform, the spectra belonging to the two classes and given as training sets can be most efficiently classified.
Another mathematical technique described in Opt. Eng., 23, 728, (1984), Z. H. Gu and S. H. Lee (Reference 2) may be utilized in place of Reference 1. According to the method of Reference 2, optimally classified vectors are obtained for two or more arbitrary classes on the basis of an evaluation reference called a hotelling trace criterion (HTC). Although Reference 2 exemplifies image classification, n-dimensional vector data is used in place of an image expressed as an n-dimensional vector by n pixels, and filter characteristics can be derived in accordance with the above theory.
A method of recognizing and classifying colors is described in a statistic technique for spectrum data in Appl. Opt., 26, 4240 (1987), J. Parkkien and T. Jaaskelainen (Reference 3). According to the method of Reference 3, analysis of major components (K-L transform) of training sets in units of classes is performed, and partial spaces of the respective classes are set. Rotation is performed to eliminate an overlapping portion between the adjacent partial spaces of the classes.
As described above, a method of classifying spectrum patterns to aim at color discrimination can be realized by applying conventional statistic pattern classification methods.
Although filter characteristics derived from any of the conventional statistic techniques are suitable for color classification, they cannot detect a color of a target object recognized with three primary colors, i.e., R, G, and B.
In order to solve this problem, as described in Reference 3, when the method of estimating an original n-dimensional vector (spectrum) from a vector projected into each class partial space is used, estimated spectra can be transformed into R, G, and B values.
In the method described in Reference 3, major component analysis must be repeated until filter characteristics are determined. That is, a covariance matrix is obtained to solve an eigenvalue problem. This operation must be repeatedly performed, and a large amount of arithmetic operations are required. Since the partial spaces are defined in units of classes, a projection operation for determining a correspondence between each vector and each specific class must be repeated in units of classes. That is, an operation for measuring a light intensity through a spectral filter must be repeated in units of classes, thus resulting in a long processing period of time.